| The rapid manufacturing of metal components uses high energy beam as heat source to melt the metallic materials which are supplied synchronously. Then the materials are free deposited layer by layer to the path set up by the computer and finally become metal components of complex shape. The rapid manufacturing of metal components is a complex hypernormal forming process of heat transfer and materials transfer, which has many parameters that can affect final quality of components such as high energy beam power, torch scanning speed, materials feed speed, working gas flow rate, position of torch, substrate shape and surface condition and so on. Thus, the online visualization for plasma spray jet morphology and the shape of deposition layer have become a necessary and important approach to assure stabilized and reliable parts quality.First, in order to study the energy related process parameters'affect on the powder and the newly emerging liquid plasma spray jet, a real-time image collection system for plasma spray process was developed. The axial ratio of the ellipse that has the same normalized second-order central moments was proposed to be the eigenvalue of plasma spray jet morphology. In the experiment, the CCD sensor was used to capture the images of spray jet which were on different spray power and working gas flow rates. The images were processed to detect the edge and calculate the jet eigenvalues in MATLAB. The results indicate that this eigenvalue can fit in the jet transformation or rotation quite well. With the spraying power increases, the jet eigenvalues become greater. With the Ar gas flow rate increases, the jet eigenvalues don't increase but decrease. The liquid spray jet eigenvalue is less than the powder spray when using the same process parameters. This system provides an important process parameters optimization approach for the rapid and low cost manufacturing of new functional components using powder or liquid plasma spray process.Second, the metal components industrial robot 3-D deposition rapid manufacture is based on off-line and teaching programming technology and thus its efficiency and precision are significantly limited. To improve the components'dimensional accuracy, forming property quality and efficiency, the technology of self-adaption path planning based on deposition height tracking and arc length self-adaption control have become the key technology of industrial robot 3-D deposition rapid manufacture. As the foundation key technology, the visualized monitor for deposition height and deposition layer shape are used not only for complex metal components rapid manufacturing, but also for rapid auto-repair of large key parts and mould.The deposition layer location and shape detection system was studied systemically based on monocular vision using a vari-focal CCD sensor. To obtain the principal optical axis length, the method that used geometry optical focusing and CCD camera model calibration technology was presented. At first, the curves determined by the relationships between physical dimension and pixel dimension of the calibration target on different object distances were found out. Then the target object distances and the deposition layer height were calculated according to the deposition layer pixel dimension. In addition, the target point 3D locating optical geometry model was set up. Based on that, the deposition layer shape detection and defect location software was developed. In the experiment, the target point images were collected from different camera pitching angles and processed in the software mentioned above. The target point 3D coordinates were calculated and compared to theoretical value. The results show that the accuracy of these locating algorithms meets the demands of practical application. The research achievements above are the applied technological foundation of not only visualized diagnostics for plasma spray jet and deposition layer shape, but the approach to assure stabilized and reliable parts quality. |
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